This book provides a comprehensive review and in-depth discussion of the state-of-the-art research literature and propose energy-efficient computation offloading and resources management for Mobile Edge Computing (MEC), covering task offloading, channel allocation, frequency scaling and resource scheduling. Since the task arrival process and channel conditions are stochastic and dynamic, the authors first propose an Energy Efficient Dynamic Computing Offloading (EEDCO) scheme to minimize energy consumption and guarantee terminal devices’ delay performance. Then, to further improve energy efficiency combined with tail energy, a Computation Offloading and Frequency Scaling for Energy Efficiency (COFSEE) scheme is presented to jointly deal with the stochastic task allocation and CPU-cycle frequency scaling to achieve the minimum energy consumption while guaranteeing the system stability. The authors also investigate delay-aware and energy-efficient computation offloading in a dynamic MEC system with multiple edge servers. An end-to-end Deep Reinforcement Learning (DRL) approach is presented as well to select the best edge server for offloading and allocate the optimal computational resource such that the expected long-term utility is maximized. Finally, the authors study the multi-task computation offloading in multi-access MEC via non-orthogonal multiple access (NOMA) and accounting for the time-varying channel conditions between the ST and edge-computing servers. An online algorithm, which is based on deep reinforcement learning (DRL) is proposed to efficiently learn the near-optimal offloading solutions.With the proliferation of mobile devices and development of Internet of Things (IoT), more and more computation-intensive and delay-sensitive applications are running on terminal devices, which result in high energy consumption and heavy computation load of devices. Due to the size and hardware constraints, the battery lifetime and computing capacity of terminal devices are limited. Consequently, it is hard to process all of tasks locally while satisfying Quality and Service (QoS) requirements for devices. Mobile Cloud Computing (MCC) is a potential technology to solve the problem, where terminal devices can alleviate operating load by offloading tasks to the cloud with abundant computing resource for processing. However, as cloud servers generally locate far away from terminal devices, data transmission from terminal devices to cloud servers would incur a large amount of energy consumption and transmission delay. Mobile Edge Computing (MEC) is considered as a promising paradigm that deploys computing resource at the network edge in proximity of terminal devices. With the help of MEC, terminal devices can achieve better computing performance and battery lifetime while ensuring QoS. The introduction of MEC also brings the challenges of computation offloading and resources management under the energy-constrained and dynamic channel conditions. It is of importance to design energy-efficient computation offloading strategies while considering the dynamics of task arrival and system environments.Researchers working in  Mobile Edge Computing, Task Offloading and Resource Management as well as advanced level students studying Electric & Computer Engineering, Telecommunications, Computer Science or other related disciplines will find this book useful as a reference. Professionals working within these related fields or consultants working in Mobile Edge Computing and Internet-Of-Things  may also be interested in this book. weiterlesen